Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning
Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-07-01
|
| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fneur.2020.00706/full |
| _version_ | 1811224460224626688 |
|---|---|
| author | Vejay N. Vakharia Vejay N. Vakharia Vejay N. Vakharia Rachel E. Sparks Alejandro Granados Anna Miserocchi Anna Miserocchi Anna Miserocchi Andrew W. McEvoy Andrew W. McEvoy Andrew W. McEvoy Sebastien Ourselin John S. Duncan John S. Duncan John S. Duncan |
| author_facet | Vejay N. Vakharia Vejay N. Vakharia Vejay N. Vakharia Rachel E. Sparks Alejandro Granados Anna Miserocchi Anna Miserocchi Anna Miserocchi Andrew W. McEvoy Andrew W. McEvoy Andrew W. McEvoy Sebastien Ourselin John S. Duncan John S. Duncan John S. Duncan |
| author_sort | Vejay N. Vakharia |
| collection | DOAJ |
| description | Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using “spatial priors” derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices.Methods: Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically.Results: All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements.Conclusions: We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed. |
| first_indexed | 2024-04-12T08:49:46Z |
| format | Article |
| id | doaj.art-c61422562b164586af35d945c26177a5 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-12T08:49:46Z |
| publishDate | 2020-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-c61422562b164586af35d945c26177a52022-12-22T03:39:36ZengFrontiers Media S.A.Frontiers in Neurology1664-22952020-07-011110.3389/fneur.2020.00706550786Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic LearningVejay N. Vakharia0Vejay N. Vakharia1Vejay N. Vakharia2Rachel E. Sparks3Alejandro Granados4Anna Miserocchi5Anna Miserocchi6Anna Miserocchi7Andrew W. McEvoy8Andrew W. McEvoy9Andrew W. McEvoy10Sebastien Ourselin11John S. Duncan12John S. Duncan13John S. Duncan14Department of Clinical and Experimental Epilepsy, University College London, London, United KingdomNational Hospital for Neurology and Neurosurgery, London, United KingdomChalfont Centre for Epilepsy, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomDepartment of Clinical and Experimental Epilepsy, University College London, London, United KingdomNational Hospital for Neurology and Neurosurgery, London, United KingdomChalfont Centre for Epilepsy, London, United KingdomDepartment of Clinical and Experimental Epilepsy, University College London, London, United KingdomNational Hospital for Neurology and Neurosurgery, London, United KingdomChalfont Centre for Epilepsy, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomDepartment of Clinical and Experimental Epilepsy, University College London, London, United KingdomNational Hospital for Neurology and Neurosurgery, London, United KingdomChalfont Centre for Epilepsy, London, United KingdomObjective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using “spatial priors” derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices.Methods: Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically.Results: All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements.Conclusions: We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.https://www.frontiersin.org/article/10.3389/fneur.2020.00706/fullstereoelectroencephalographyEpiNavcomputer-assisted planningmachine learningspatial priorsepilepsy surgery |
| spellingShingle | Vejay N. Vakharia Vejay N. Vakharia Vejay N. Vakharia Rachel E. Sparks Alejandro Granados Anna Miserocchi Anna Miserocchi Anna Miserocchi Andrew W. McEvoy Andrew W. McEvoy Andrew W. McEvoy Sebastien Ourselin John S. Duncan John S. Duncan John S. Duncan Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning Frontiers in Neurology stereoelectroencephalography EpiNav computer-assisted planning machine learning spatial priors epilepsy surgery |
| title | Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning |
| title_full | Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning |
| title_fullStr | Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning |
| title_full_unstemmed | Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning |
| title_short | Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning |
| title_sort | refining planning for stereoelectroencephalography a prospective validation of spatial priors for computer assisted planning with application of dynamic learning |
| topic | stereoelectroencephalography EpiNav computer-assisted planning machine learning spatial priors epilepsy surgery |
| url | https://www.frontiersin.org/article/10.3389/fneur.2020.00706/full |
| work_keys_str_mv | AT vejaynvakharia refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT vejaynvakharia refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT vejaynvakharia refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT rachelesparks refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT alejandrogranados refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT annamiserocchi refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT annamiserocchi refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT annamiserocchi refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT andrewwmcevoy refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT andrewwmcevoy refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT andrewwmcevoy refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT sebastienourselin refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT johnsduncan refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT johnsduncan refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning AT johnsduncan refiningplanningforstereoelectroencephalographyaprospectivevalidationofspatialpriorsforcomputerassistedplanningwithapplicationofdynamiclearning |